A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

Last updated: 2026-01-19

Get an M1 Mac ready for daily work with Claude Code, Codex CLI, ZSH + dotfiles, Postgres 17, Redis, and the Diversio monolith. Every step is copy/paste friendly with a clear "why" and a quick success check.

Follow these instructions for an easy way to get up and going quickly! These are complete instructions, and will be the easiest way to get started on a new RG35XX.

Get a high quality SD (e.g. SanDisk Extreme) card, 128GB or larger, 256GB is recommended. Don't skimp here, they're cheap, and don't use the card that comes with the RG35XX as it's crap.

Here's an overview of my Factorio ship designs which can be built inexpensively.

See also the Design topics section below.

These ships are designed for use within the first four planets.

• The Hornet (the cheapest useful ship)

Please make me a rich, interactive explanation of the specified code change.

It should have these sections:

So here's the premise: For scenes that take around a minute or less to render, performance is actually worse if you render on all of the cards with a single instance of Blender. This is because (AFAIK) there's a bit of additional time necessary to collect the render results from each card and stitch them together. That time is a fixed short duration, so it's negligible on larger/longer render jobs. However, on shorter render jobs, the 'stitch time' has a much more significant impact.

I ran into this with a machine I render on that has 4 Quadro K6000s in it. To render animations, I ended up writing a few little scripts to facilitate launching 4 separate instances of Blender, each one tied to one GPU. Overall rendertime was much shorter with that setup than one instance of Blender using all 4 GPUs.

The setup works basically like this... I have a the following Python script (it can be anywhere on your hard drive, so long as you remember the path to it).

Over the weekend, I decided to try running a Postgres database in my Homelab. In my current setup, the most convenient option for storage is NFS. However, NFS is especially tricky for databases. A misconfigured setup can lead to performance or data corruption issues.

After watching